ACM Home Page
Please provide us with feedback. Feedback
Physically guided liquid surface modeling from videos
Full text PdfPdf (5.43 MB)
Source
ACM Transactions on Graphics (TOG) archive
Volume 28 ,  Issue 3  (August 2009) table of contents
Proceedings of ACM SIGGRAPH 2009
SESSION: Physically based modeling: from contact to capture table of contents
Article No. 90  
Year of Publication: 2009
ISSN:0730-0301
Also published in ...
Authors
Huamin Wang  Georgia Institute of Technology
Miao Liao  University of Kentucky
Qing Zhang  University of Kentucky
Ruigang Yang  University of Kentucky
Greg Turk  Georgia Institute of Technology
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 63,   Downloads (12 Months): 145,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1531326.1531396
What is a DOI?

ABSTRACT

We present an image-based reconstruction framework to model real water scenes captured by stereoscopic video. In contrast to many image-based modeling techniques that rely on user interaction to obtain high-quality 3D models, we instead apply automatically calculated physically-based constraints to refine the initial model. The combination of image-based reconstruction with physically-based simulation allows us to model complex and dynamic objects such as fluid. Using a depth map sequence as initial conditions, we use a physically based approach that automatically fills in missing regions, removes outliers, and refines the geometric shape so that the final 3D model is consistent to both the input video data and the laws of physics. Physically-guided modeling also makes interpolation or extrapolation in the space-time domain possible, and even allows the fusion of depth maps that were taken at different times or viewpoints. We demonstrated the effectiveness of our framework with a number of real scenes, all captured using only a single pair of cameras.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
2
3
4
5
 
6
7
8
9
 
10
 
11
Grant, I. 1997. Particle image velocimetry: a review. In Proc. of the Institution of Mechanical Engineers, vol. 211, 55C76.
12
13
 
14
 
15
Kanade, T., Rander, P., Vedula, S., and Saito, H. 1999. Virtualized reality: Digitizing a 3d time-varying event as is and in real time. In Mixed Reality, Merging Real and Virtual Worlds. 41--57.
16
 
17
 
18
 
19
Morris, N. J. W., and Kutulakos, K. N. 2007. Reconstructing the surface of inhomogeneous transparent scenes by scatter trace photography. In Proc. of 11th Int. Conf. Computer Vision.
20
 
21
Schneider, R., and Kobbelt, L. 2001. Geometric fairing of irregular meshes for freeform surface design. Computer aided geometric design 18, 359--379.
22
 
23
Shi, J., and Tomasi, C. 1994. Good features to track. In Proc. of CVPR 1994, 593--600.
 
24
Simon, S. V., Baker, S., Seitz, S., and Kanade, T. 2000. Shape and motion carving in 6d. In Computer Vision and Pattern Recognition.
25
 
26
 
27
Staniforth, A., and Côté, J. 1991. Semi-lagrangian integration schemes for atmospheric models. Monthly Weather Review 119, 9, 2206.
 
28
29
 
30
31
32
33
 
34
Yang, Q., Yang, R., Davis, J., and Nister, D. 2007. Spatial-depth super resolution for range images. In Proc. of CVPR 2007, vol. 0, 1--8.
35
36

Collaborative Colleagues:
Huamin Wang: colleagues
Miao Liao: colleagues
Qing Zhang: colleagues
Ruigang Yang: colleagues
Greg Turk: colleagues